Data-Driven Process Mining for Automated Compliance Monitoring Using AI Algorithms
Keywords:
data-driven process mining, artificial intelligenceAbstract
Data-driven process mining, enhanced by artificial intelligence (AI) algorithms, offers transformative potential for automated compliance monitoring, particularly within highly regulated sectors such as finance, healthcare, and legal industries. This paper explores the integration of AI techniques with process mining to provide a scalable and efficient solution for ensuring regulatory adherence. Process mining leverages event logs to visualize, analyze, and optimize organizational workflows, and when coupled with AI, it enables real-time monitoring and the automated identification of non-compliance. The research examines various AI algorithms, such as machine learning models and neural networks, in detecting deviations from established compliance protocols. Furthermore, it highlights how these technologies facilitate continuous monitoring and auditing, reducing human error and enhancing the transparency of compliance processes. Practical case studies from finance and healthcare sectors illustrate the effectiveness of AI-powered process mining in identifying compliance risks and streamlining regulatory checks. Challenges such as data privacy, algorithmic transparency, and the need for high-quality data are discussed, alongside future research directions for improving the precision and adaptability of these systems. Overall, this paper contributes to the growing body of knowledge on the use of AI in automating and optimizing compliance monitoring processes across industries.
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